Retrospective Cohort Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jun 15, 2025; 17(6): 106608
Published online Jun 15, 2025. doi: 10.4251/wjgo.v17.i6.106608
Preoperative model for predicting early recurrence in hepatocellular carcinoma patients using radiomics and deep learning: A multicenter study
Yong-Hai Li, Gui-Xiang Qian, Ling Yao, Xue-Di Lei, Yu Zhu, Lei Tang, Zi-Ling Xu, Xiang-Yi Bu, Ming-Tong Wei, Jian-Lin Lu, Wei-Dong Jia
Yong-Hai Li, Wei-Dong Jia, Cheeloo College of Medicine, Shandong University, Jinan 250021, Shandong Province, China
Yong-Hai Li, Wei-Dong Jia, The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China, Hefei 230001, Anhui Province, China
Yong-Hai Li, Gui-Xiang Qian, The First People's Hospital of Hefei, The Third Affiliated Hospital of Anhui Medical University, Hefei 230001, Anhui Province, China
Ling Yao, Department of Anorectal, Chinese Academy of Traditional Chinese Medicine, Xiyuan Hospital, Beijing 100091, China
Xue-Di Lei, Department of Colorectal Surgery, Bengbu Medical University, Bengbu 233000, Anhui Province, China
Yu Zhu, Department of Hepatopancreatobiliary Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou 318000, Zhejiang Province, China
Lei Tang, Department of Infectious Disease, The Second Hospital of Anhui Medical Univercity, Hefei 230001, Anhui Province, China
Zi-Ling Xu, Ming-Tong Wei, Jian-Lin Lu, Department of Hepatic Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei 230001, Anhui Province, China
Xiang-Yi Bu, Division of Life Science and Medicine, Department of Hepatic Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China, Hefei 230001, Anhui Province, China
Co-first authors: Yong-Hai Li and Gui-Xiang Qian.
Author contributions: Li YH, Qian GX, Yao L and Jia WD revised the manuscript; Li YH, Qian GX, Yao L, Lei XD, Zhu Y, and Tang L collected the data; Li YH and Jia WD designed the research study; Xu ZL, Bo XY, Wei MT and Lu JL analyzed the data; all authors wrote the manuscript, have read and approve the final manuscript.
Supported by Anhui Provincial Key Research and Development Plan, No. 202104j07020048.
Institutional review board statement: This study was approved by the Ethics Management Committee of The First Affiliated Hospital of University of Science and Technology of China, No. 2021-RE-043.
Informed consent statement: The institutional ethics review board has approved our study, and the requirement for informed consent was waived because of the retrospective nature of the study.
Conflict-of-interest statement: The authors declare that they have no financial conflicts of interest with regard to the content of this study.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: The datasets generated and/or analysed during the current study are not publicly available due to patient privacy and copyright issues but are available from the corresponding author upon reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Wei-Dong Jia, MD, PhD, Professor, Cheeloo College of Medicine, Shandong University, No. 44 Wen Hua Xi Road, Jinan 250021, Shandong Province, China. jwd1968@ustc.edu.cn
Received: March 3, 2025
Revised: April 19, 2025
Accepted: May 6, 2025
Published online: June 15, 2025
Processing time: 103 Days and 9.8 Hours
Abstract
BACKGROUND

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Ablation therapy is one of the first-line treatments for early HCC. Accurately predicting early recurrence (ER) is crucial for making precise treatment plans and improving patient prognosis.

AIM

To establish an intratumoral and peritumoral model for predicting ER in HCC patients following curative ablation.

METHODS

This study included a total of 288 patients from three Centers. The patients were divided into a primary cohort (n = 222) and an external cohort (n = 66). Radiomics and deep learning methods were combined for feature extraction, and models were constructed following a three-step feature selection process. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), while calibration curves and decision curve analysis (DCA) were used to assess calibration and clinical utility. Finally, Kaplan-Meier (K-M) analysis was used to stratify patients according to progression-free survival (PFS) and overall survival (OS).

RESULTS

The combined model, which utilizes the light gradient boosting machine learning algorithm and incorporates both intratumoral and peritumoral regions (5 mm and 10 mm), demonstrated the best predictive performance for ER following HCC ablation, achieving AUCs of 0.924 in the training set, 0.899 in the internal validation set, and 0.839 in the external validation set. Calibration and DCA curves confirmed strong calibration and clinical utility, whereas K-M curves provided risk stratification for PFS and OS in HCC patients.

CONCLUSION

The most efficient model integrated the tumor region with the peritumoral 5 mm and 10 mm regions. This model provides a noninvasive, effective, and reliable method for predicting ER after curative ablation of HCC.

Keywords: Hepatocellular carcinoma; Ablation; Early recurrence; Radiomics; Deep learning; Peritumoral

Core Tip: This study developed a predictive model for early recurrence (ER) in hepatocellular carcinoma (HCC) patients postablation by combining radiomics and deep learning. The model, which integrates intratumoral and peritumoral regions, demonstrated strong predictive performance, with area under the receiver operating characteristic curve of 0.924, 0.899, and 0.839 in the training, internal, and external validation sets, respectively. It offers a noninvasive and reliable method for ER prediction, providing valuable insights for treatment planning and prognosis in HCC patients.